# -*- coding: utf-8 -*-
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from datetime import timedelta
from jms.dwd.dwd_terminal_sign_all_detail_new_dt import jms_dwd__dwd_terminal_sign_all_detail_new_dt

jms_dm__dm_terminal_sign_detail_new_dt = SparkSqlOperator(
    task_id='jms_dm__dm_terminal_sign_detail_new_dt',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    #execution_timeout=timedelta(hours=2)
    #excel平均时长:14分13秒
    execution_timeout = timedelta(minutes=30),
    email=['houwenlong@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='jms_dm__dm_terminal_sign_detail_new_dt_{{ execution_date| date_add(1) | cst_ds }}',
    sql='jms/dm/dm_terminal_sign_detail_new_dt/execute.hql',
    executor_cores=2,
    executor_memory='10G',
    num_executors=50,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={
        'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
        'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
        'spark.dynamicAllocation.maxExecutors': 100,  # 动态资源最大扩容 Executor 数
        'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
        'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
        'spark.executor.memoryOverhead': '2G',  # 堆外内存
        'spark.sql.shuffle.partitions': 600
    },
    hiveconf={
        'hive.exec.dynamic.partition': 'true',  # 动态分区
        'hive.exec.dynamic.partition.mode': 'nonstrict',
        'hive.exec.max.dynamic.partitions': 400,  # 每天生成 20 个分区
        'hive.exec.max.dynamic.partitions.pernode': 400,  # 每天生成 20 个分区
    },
)

jms_dm__dm_terminal_sign_detail_new_dt << [
    jms_dwd__dwd_terminal_sign_all_detail_new_dt
]


